US8687044B2 - Depth camera compatibility - Google Patents

Depth camera compatibility Download PDF

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Publication number
US8687044B2
US8687044B2 US12/698,940 US69894010A US8687044B2 US 8687044 B2 US8687044 B2 US 8687044B2 US 69894010 A US69894010 A US 69894010A US 8687044 B2 US8687044 B2 US 8687044B2
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depth
depth image
camera
virtual
native
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US20110187819A1 (en
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Sagi Katz
Avishai Adler
Guy Gilboa
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Microsoft Technology Licensing LLC
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Microsoft Corp
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Priority to JP2012551984A priority patent/JP5778182B2/ja
Priority to CN201180007815.7A priority patent/CN102763420B/zh
Priority to KR1020127020257A priority patent/KR101783379B1/ko
Priority to CA2786436A priority patent/CA2786436C/en
Priority to PCT/US2011/020690 priority patent/WO2011097049A2/en
Priority to EP11740159.6A priority patent/EP2531979B1/de
Publication of US20110187819A1 publication Critical patent/US20110187819A1/en
Priority to HK13104543.9A priority patent/HK1177311A1/zh
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/139—Format conversion, e.g. of frame-rate or size
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/128—Adjusting depth or disparity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/204—Image signal generators using stereoscopic image cameras
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/271—Image signal generators wherein the generated image signals comprise depth maps or disparity maps
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/30—Image reproducers
    • H04N2013/40—Privacy aspects, i.e. devices showing different images to different viewers, the images not being viewpoints of the same scene
    • H04N2013/405—Privacy aspects, i.e. devices showing different images to different viewers, the images not being viewpoints of the same scene the images being stereoscopic or three dimensional

Definitions

  • Cameras can be used to capture still images of a scene.
  • Several still images taken in rapid succession can be used to generate a movie including a plurality of frames, each frame corresponding to a different still image. While such images are very useful in a variety of different applications, such images are not well suited for some purposes.
  • conventional still images and movies do not provide adequate information to accurately assess the relative depths of the various surfaces captured in the scene.
  • Different types of depth cameras have been developed to fill this need.
  • the various different types of depth cameras may produce depth images that differ from one another in one or more respects. As such, applications that consume depth images may only be compatible with a particular type of depth camera that produces depth images with expected characteristics.
  • compatibility between a depth image consumer and a plurality of different depth image producers may be provided by receiving a native depth image having unsupported depth camera parameters that are not compatible with a depth image consumer, and converting the native depth image to a virtual depth image having supported virtual depth camera parameters that are compatible with the depth image consumer. This virtual depth image may then be output to the depth image consumer.
  • FIG. 1 shows an example depth camera usage environment.
  • FIG. 2 shows a depth camera interchangeability system in accordance with an embodiment of the present disclosure.
  • FIGS. 3A and 3B schematically show depth camera view frustums.
  • FIGS. 4A-4C show example depth image producers in accordance with the present disclosure.
  • FIG. 5 shows an example method of providing compatibility between a depth image consumer and a plurality of different depth image producers.
  • FIG. 6 shows another example method of providing depth camera compatibility.
  • FIG. 7 schematically shows an example of a shadow effect created on a sensor which is distanced from a light source.
  • FIG. 8 schematically shows an example plot illustrating the finding of shadowed pixels via a shadow synthesis algorithm.
  • FIG. 9 schematically shows an example image processing block within a depth abstraction layer.
  • FIG. 10 schematically shows the example image processing block of FIG. 9 in more detail.
  • FIG. 11 schematically shows a computing system in accordance with an embodiment of the present disclosure.
  • the present disclosure is directed to depth camera compatibility.
  • Compatibility technology allows different models of depth cameras to be used by the same computing system.
  • An application developer may easily create applications that are compatible with a variety of different depth cameras, including depth cameras that do not exist at the time the application developer creates an application.
  • an application developer may create an application that is configured to work with a generic virtual depth camera, and the compatibility technology can transform native input from a variety of different depth cameras into a form that is compatible with the generic virtual depth camera.
  • an application can be compatible with a variety of different depth cameras, including depth cameras that utilize completely different depth-finding technologies (e.g., structured light, time of flight, stereo vision, etc.).
  • an application developer need not be concerned with the tedious and difficult job of supporting a variety of different depth cameras, but instead may develop for a single generic virtual depth camera.
  • the compatibility technology facilitates the development and implementation of new depth cameras that may offer improved performance, lower manufacturing and/or operating costs, improved energy efficiency, and/or other useful characteristics.
  • new camera technologies may be implemented in new cameras that are backward compatible with previously developed applications.
  • FIG. 1 shows a nonlimiting example of a computing system 10 that may be designed with a depth camera interchangeability system that provides camera compatibility as introduced above.
  • FIG. 1 shows a gaming console 12 that may be used to play a variety of different games, play one or more different media types, and/or control or manipulate non-game applications.
  • FIG. 1 also shows a display 14 in the form of a high-definition television, or HDTV 16 , which may be used to present visual information to users, such as user 18 .
  • FIG. 1 shows a capture device in the form of a depth camera 20 , which may be used to visually monitor one or more users, such as user 18 . While described in the context of a gaming system, it is to be understood that depth camera interchangeability systems may be implemented in virtually any computing system configured to use depth images from depth cameras.
  • a computing system such as gaming console 12 , may be used to recognize, analyze, and/or track one or more targets, such as user 18 .
  • Target movements may be interpreted as operating system and/or application controls.
  • Virtually any controllable aspect of an operating system and/or application may be controlled by movements of a target, such as user 18 .
  • depth information acquired via depth camera 20 may be used for purposes other than tracking a target.
  • depth camera 20 may be used to visually survey a room and/or one or more objects.
  • Depth information acquired from the depth camera 20 may be used to create a three-dimensional computer-readable model of the room and/or object(s) being surveyed.
  • a depth camera may be used as a proximity sensor on a moving computing system, such as a vehicle or a robot.
  • a depth camera such as depth camera 20
  • depth camera 20 may be used to provide these and a variety of other useful functions.
  • applications can be developed and deployed for providing any number of different functions that take advantage of depth information from a depth camera.
  • most applications include complicated algorithms and processing strategies for deducing real world characteristics of a viewed scene from the depth information.
  • skeletal tracking algorithms and processing strategies may be employed to model a target, such as user 18 , with a machine representation of a skeleton that tracks the target.
  • floor finding algorithms may be employed to find a floor within a scene.
  • Applications that do not utilize the herein described camera compatibility technology may be designed to receive depth information in a particular format and/or with particular parameters. Such an application may not be able to use depth information that does not match the desired format and/or parameters. In some cases, depth information that does not match the desired format and/or parameters may crash such an application.
  • Each different type of depth camera may produce depth information having one or more formatting differences and/or different parameters.
  • applications up until now applications have been designed with a particular depth camera in mind.
  • that application when an application is designed to be used with only a particular depth camera, that application may not be able to support new cameras, which may output depth information in an unfamiliar format and/or with unexpected parameters.
  • FIG. 2 schematically shows a depth camera interchangeability system 30 that provides compatibility between a depth image consumer 32 and a plurality of different depth image producers 34 (e.g., depth image producer 34 a , depth image producer 34 b , and depth image producer 34 c ).
  • a depth image consumer refers to any operating system, application, service, process, module, engine, or other object that is configured to utilize depth information from a depth camera.
  • Depth camera interchangeability system 30 provides a great deal of flexibility with respect to which models of depth cameras can be used with depth image consumers. Cameras having different parameters, cameras based on different depth finding technologies, and cameras producing different types of depth images may be interchangeably used with depth camera interchangeability system 30 .
  • depth image producer 34 a may include a depth camera that uses structured light technology to assess depth images; and depth image producer 34 b may include a depth camera that uses time of flight technology to assess depth images. Both such cameras are compatible with depth image consumer 32 because of depth camera interchangeability system 30 .
  • Depth image consumer 32 need not have been specifically designed for compatibility with either camera. Depth image consumer 32 may be developed before either camera is released. In other words, depth camera interchangeability system 30 facilitates compatibility with depth image consumers and a variety of different depth cameras, including depth cameras developed after the depth image consumer is developed.
  • depth camera interchangeability system 30 includes an initialization module 36 , a capture module 38 , a virtualization module 40 , and an output module 42 .
  • the initialization module, the capture module, the virtualization module, the output module, and/or other components of depth camera interchangeability system 30 may be implemented as part of an application programming interface (API).
  • An API may be implemented by operating systems, applications, libraries, and/or other objects to establish the communication conventions to be used between a depth image producer and a depth image consumer.
  • An API may include specifications for routines, data structures, object classes and protocols used to communicate between the depth image producer and the depth image consumer.
  • the API may include, or interface with, functional blocks configured to process various aspects of a depth image, as described below with reference to virtualization module 40 and emulation module 44 , for example.
  • Initialization module 36 may be configured to acknowledge supported virtual depth camera parameters for a depth image consumer, such as depth image consumer 32 .
  • a depth image consumer such as a skeletal tracking game application, may be developed for compatibility with a virtual depth camera in addition to, or instead of, a particular depth camera (e.g., a structured light depth camera of a particular make and model).
  • the depth image consumer may be configured to provide an indication of the virtual depth camera parameters expected by that depth image consumer (e.g., x-y-z resolution, view frustum, depth image type, etc.).
  • the initialization module may be configured to receive an indication of the supported virtual depth camera parameters from the depth image consumer.
  • the initialization module may optionally be implemented as part of an API that the depth image consumer and depth image producer can use to communicate with one another.
  • the indication of the supported virtual depth camera parameters may specify an expected view frustum of a virtual depth camera and/or an expected resolution of a virtual depth camera. Such an expected view frustum and/or expected resolution may be different than the native view frustum and native resolution of a particular depth camera.
  • the indication of the supported virtual depth camera parameters may specify a model of an actual depth camera on which a virtual depth camera is based. The following description summarizes a nonlimiting sampling of different parameters that may be acknowledged by the initialization module 36 .
  • Depth cameras generally measure at each pixel the radial distance from a nearest surface to the depth camera sensor. This distance may be converted into world coordinates by projecting the distance along rays from the depth camera into a three dimensional space modeling the real world. This process produces a range of three-dimensional samples for which tight linear boundaries may not exist. Instead, the points are bounded by the view frustum of the depth camera—the space caught between two three-dimensional arcs of two concentric balls (assuming no lens distortions).
  • FIG. 3A shows an example view frustum 46 for an example depth camera 48 .
  • the position of the concentric balls is determined by the depth of field measurable by the depth camera.
  • the smaller of the concentric balls has a radius that is equivalent to the minimum measurable distance for that depth camera, and the larger of the concentric balls has a radius that is equivalent to the maximum measurable distance for that depth camera.
  • the size of the three-dimensional arcs in space is determined by the field of view (i.e., view angles) for that depth camera.
  • the position and direction of the view frustum of the depth camera is determined by the physical position and physical orientation of the depth camera. How many different pixels are included in the view frustum is determined by the resolution of the depth camera.
  • the sensitivity (resolution) of the depth depends on the number of bits per pixel that are received as output from the depth camera. However, the actual sensitivity may be lower.
  • the parameters may be different for different cameras.
  • the difference in parameters between cameras can pose difficulties because depth image consumers may be highly sensitive to such differences.
  • a depth image consumer may be developed for compatibility with a virtual depth camera having virtual depth camera parameters.
  • the depth camera interchangeability system may effectively translate depth information received from an actual camera in accordance with the virtual parameters of the virtual depth camera so that the depth information from the actual camera may be used by the depth image consumer.
  • the initialization module 36 may be configured to generate aiming instructions 37 to facilitate aiming a depth camera for compatibility with the supported virtual depth camera parameters of the depth image consumer.
  • a depth image consumer may expect a depth camera to be aimed down toward a floor so that the feet of a user can be scanned.
  • aiming instructions can be generated to communicate this expectation.
  • the initialization module may be configured to send the aiming instructions to the depth camera so that the depth camera may reposition itself in accordance with the aiming instructions.
  • the aiming instructions may be conveyed to a user via visual and/or audio instructions so that a user may manually reposition the depth camera.
  • a coordinate system may be abstracted so that a depth image consumer will receive depth images having an expected coordinate system. In some instances, this may involve rotating the coordinate system to be aligned with the floor (for example), by using hardware that measures the line of sight, or by measuring the angle of the floor relative to the camera.
  • Capture module 38 may be configured to receive a native depth image from a depth image producer.
  • the capture module may receive a native depth image from a depth image producer including a time of flight camera.
  • the capture module may optionally be implemented as part of an API that the depth image consumer and depth image producer can use to communicate with one another.
  • a depth image producer may include only a depth camera, or a depth image producer may include a depth camera as well as off-board processing engines.
  • a depth image producer 34 ′ may include an on-camera processor 52 configured to translate raw depth camera data (not shown) into a native depth image 54 .
  • the capture module 38 ′ may be configured to receive the native depth image 54 via the on-camera processor 52 .
  • a depth image producer 34 ′′ may include an off-camera processor 56 that translates raw depth camera data 60 into a native depth image 54 ′′.
  • the off-camera processor 56 may be part of a computing system 10 ′′ on which a depth image consumer 32 ′′ is running, for example.
  • the capture module 38 ′′ may be configured to receive the native depth image via the off-camera processor 56 .
  • a depth image producer 34 ′′′ may include an on-camera processor 52 ′′′ and an off-camera processor 56 ′′′ that cooperate to translate raw depth camera data (not shown) into a native depth image 54 ′′′.
  • the capture module 38 ′′′ may be configured to receive the native depth image 54 ′′′ via the on-camera processor 52 ′′′ and the off-camera processor 56 ′′′.
  • the on-camera processor 52 ′′′ may pass the off-camera processor 56 ′′′ intermediate data 62 that has been partially processed from the raw depth camera data.
  • the off-camera processor 56 ′′′ may be part of a computing system 10 ′′′ on which a depth image consumer 32 ′′′ is running, for example.
  • virtualization module 40 may be configured to convert the native depth image to a virtual depth image having supported virtual depth camera parameters compatible with the depth image consumer.
  • the virtualization module may be configured to convert the native depth image to a virtual depth image in accordance with the indication of the virtual parameters received via the initialization module from the depth image consumer.
  • the virtualization module may optionally be implemented as part of an API, or as an application or service that is compatible with the API that the depth image consumer and depth image producer use to communicate.
  • the virtualization module 40 may be configured to convert the native depth image to the virtual depth image by clipping a view frustum of the native depth image.
  • a virtual view frustum 70 is illustrated in solid lines.
  • Virtual view frustum 70 can be specified by virtual depth camera parameters.
  • Superimposed in the same view is a native view frustum 72 of an actual depth camera 74 .
  • the native view frustum 72 is illustrated with dashed lines.
  • the native view frustum 72 has a wider field of view and a deeper depth of field than virtual view frustum 70 .
  • the native view frustum 72 may not be compatible for a depth image consumer expecting virtual view frustum 70 .
  • the virtualization module may clip the native view frustum to that of the virtual view frustum.
  • depth information read by the depth camera 74 that is outside the field of view and/or depth of field of the virtual view frustum may be removed from the depth image.
  • the removed depth information may simply be ignored while depth information in the virtual view frustum is left unchanged.
  • the removed depth information may be used to selectively modify depth information in the virtual view frustum.
  • the virtualization module 40 may be configured to convert the native depth image to the virtual depth image by changing a resolution of the native depth image (e.g., decreasing a resolution of the native depth image).
  • Virtually any resampling algorithm may be used to change the resolution.
  • a nearest-neighbor algorithm may be used in which a sample grid having a desired resolution is conceptually aligned with the depth image from the actual depth camera. Each sample pixel of the sample grid may be assigned the depth value of the pixel from the actual depth camera that is nearest to the sample pixel.
  • resampling algorithms may take an average, or distance weighted average, of nearest pixels.
  • a depth image consumer may be designed to expect a virtual camera with any number of different virtual parameters, and an actual depth image may be converted in accordance with such parameters in order to achieve compatibility with the virtual depth camera of the depth image consumer.
  • virtualization module 40 may include an emulation module 44 configured to convert the native depth image to the virtual depth image by processing the native depth image into an emulation depth image having a supported type that is supported by the depth image consumer.
  • a depth image consumer may be designed to expect depth images that are created using a specific model of depth camera and/or a specific depth acquisition technology (e.g., structured light or time of flight). While depth images from different types of depth cameras may ultimately produce depth images in which each pixel is assigned a depth value, differences between the different types of depth cameras may result in various differences between the depth values that are assigned to each pixel.
  • a depth image consumer may be designed to process a particular type of depth image from a particular depth camera. As such, emulation module 44 may be configured to change a depth image from an unsupported depth camera to seem as if it originated from a supported depth camera.
  • the emulation module 44 may convert a native depth image from a time of flight depth camera to an emulation depth image emulating a depth image produced by a structured light depth camera.
  • This example is not limiting.
  • the emulation module 44 may be configured to convert a native depth image from virtually any camera technology to an emulation depth image emulating a depth image produced by virtually any other type of camera technology.
  • emulation module 44 may be part of a virtualization module 40 in some embodiments, it is to be understood that the emulation techniques described herein may be performed independently of any other virtualization techniques, such as view frustum clipping and/or resolution changing. In either case, the emulation module 44 may optionally be implemented as part of an API, or as an application or service that is compatible with the API that the depth image consumer and depth image producer use to communicate.
  • an emulation module may include one or more of the following: a noise suppression module 80 , an edge enhancement module 82 , an invalidation module 84 , a depth quantization module 86 , a small object correction module 88 , and a shadow simulation module 90 .
  • Noise suppression module 80 may be configured to preserve depth edges between adjacent pixel regions with different depth values in the native depth image. The functionality of noise suppression module 80 is described in more detail below with reference to 118 of FIG. 6 .
  • Edge enhancement module 82 may be configured to enhance depth edges between adjacent pixel regions with different depth values in the native depth image. The functionality of edge enhancement module 82 is described in more detail below with reference to 126 of FIG. 6 .
  • Invalidation module 84 may be configured to invalidate pixels having a combined illumination and obliqueness outside a predetermined range. The functionality of invalidation module 84 is described in more detail below with reference to 120 - 124 of FIG. 6 .
  • Depth quantization module 86 may be configured to quantize depth values. The functionality of depth quantization module 86 is described in more detail below with reference to 128 of FIG. 6 .
  • Small object correction module 88 may be configured to assign deeper depth values to pixels belonging to objects below a threshold size. The functionality of small object correction module 88 is described in more detail below with reference to 130 of FIG. 6 .
  • Shadow simulation module 90 may be configured to assign shadow pixel values to pixels that are virtually occluded from a virtual vantage point of a virtual illuminator virtually spaced away from a time of flight depth camera. The functionality of shadow simulation module 90 is described in more detail below with reference to 132 of FIG. 6 .
  • the virtualization module 40 converts the native depth image from an actual depth camera to a virtual depth image having supported virtual depth camera parameters compatible with the depth image consumer.
  • An output module 42 is configured to output this virtual depth image to the depth image consumer.
  • the depth image consumer may then receive the virtual depth image for analysis and/or further processing.
  • the output module may optionally be implemented as part of an API that the depth image consumer and depth image producer can use to communicate with one another. While the initialization module 36 , the capture module 38 , the virtualization module 40 , and the output module 42 are described above as being discrete modules, it is to be understood that two or more of the modules may be operatively combined into a common API.
  • FIG. 5 shows a method 100 of providing compatibility between a depth image consumer and a plurality of different depth image producers.
  • Method 100 may be implemented, for example, using depth camera interchangeability system 30 of FIG. 2 .
  • method 100 includes receiving an indication of the supported virtual depth camera parameters.
  • virtual depth camera parameters may specify a view frustum of a virtual depth camera, a resolution of a virtual depth camera, a model of an actual depth camera on which a virtual depth camera is based, and/or other characteristics of the virtual depth camera.
  • method 100 includes receiving a native depth image having unsupported depth camera parameters that are not compatible with a depth image consumer.
  • method 100 includes converting the native depth image to a virtual depth image having supported virtual depth camera parameters that are compatible with the depth image consumer.
  • converting the native depth image may include one or more of clipping a view frustum of the native depth image, decreasing a resolution of the native depth image, and/or processing the native depth image into an emulation depth image having a supported type that is supported by the depth image consumer.
  • method 100 includes outputting the virtual depth image to the depth image consumer. Because the virtual depth image is specifically tailored to the supported virtual depth camera parameters, it can be used by the depth image consumer. However, because method 100 can be applied to a variety of different native depth images from different depth cameras, the depth image consumer is not restricted to receiving depth images from a single particular type of depth camera.
  • FIG. 6 shows another method 110 of providing depth camera compatibility.
  • method 110 includes receiving from a depth image consumer supported virtual depth camera parameters for the depth image consumer. This may include, for example, receiving an indication of a model depth camera (e.g., a structured light depth camera) that is compatible with the depth image consumer.
  • a model depth camera e.g., a structured light depth camera
  • method 110 includes receiving a native depth image having an unsupported type that is not supported by the depth image consumer.
  • the native depth image may be from a source other than the model depth camera.
  • the native depth image may be incompatible with the depth image consumer and thus have an unsupported type.
  • the depth image consumer may support depth images from a structured light depth camera.
  • the native depth image may be received from another source such as a time of flight depth camera, and thus the native depth image has an unsupported type for the depth image consumer.
  • method 110 includes processing the native depth image into an emulation depth image having a supported type that is supported by the depth image consumer.
  • the emulation depth image can be modified to emulate a depth image that is compatible with the depth image consumer, such as a depth image produced by the model depth camera.
  • a native depth image received from a time of flight depth camera may be processed into an emulation depth image which emulates a depth image produced by a structured light depth camera.
  • processing the native depth image into an emulation depth image may utilize a variety of techniques, as described in more detail below. It can be appreciated that such techniques are nonlimiting. Further, additional techniques not shown in FIG. 6 may alternatively or additionally be applied.
  • method 110 may include applying an edge preserving filter to the native depth image.
  • a native depth image received from a time of flight depth camera such a native depth image may have random noise that is a standard byproduct of time of flight depth cameras.
  • structured light depth cameras inherently have a smoother signal, and such a signal may even be further filtered in software.
  • a native depth image received from a time of flight depth camera may be processed to suppress noise from the time of flight depth map without compromising significant depth features.
  • an edge preserving filter may be used to suppress the noise from the native depth image.
  • any suitable approach may be used, such as by utilizing a nonlinear partial differential equation based off of those described in the works of Perona-Malik (Scale-Space and Edge Detection Using Anisotropic Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 12 n. 7, p. 629-639, 1990) and Weickert et al. (J. Weickert, B. M. ter Haar Romeny, M. A. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Trans. Image Proc., v. 7 n. 3, pp. 398-410, 1998).
  • method 110 may include building a confidence map by passing a median filter on an illumination image (e.g., as measured from the IR light used to illuminate the scene in time of flight analysis).
  • an illumination image e.g., as measured from the IR light used to illuminate the scene in time of flight analysis.
  • a confidence map may be utilized to emulate a pixel invalidation phenomenon that occurs in structured light depth cameras.
  • depth is computed by finding pattern matches; if a match is found then the depth can be computed relatively accurately, however, if a match is not found then the depth cannot be computed and the measurement at that pixel is invalid.
  • a confidence map may be built, using an illumination image as an input.
  • the confidence map may be built by first passing a median filter on the illumination image, to remove outliers and suppress noise. Then, for each pixel, a soft threshold function can be used, such as
  • F k ⁇ ( s ) 1 1 + ( s / k ) 2 wherein the parameter k may be changed between camera versions.
  • k may be set to 60 in some embodiments.
  • method 110 may include building an oblique surface map from the native depth image.
  • the pixel invalidation phenomenon occurring in structured light depth cameras may be further emulated by identifying regions likely to correspond to pattern matching difficulties.
  • the patterns projected by a structured light illumination device may be smeared on oblique objects (i.e., surfaces of sharp angles, with respect to the illumination rays), and thus, pattern matches often fail there and yield invalid measurements.
  • method 110 may include unifying the confidence map and the oblique surface map into an invalidation testing map.
  • the two maps may be unified, for example, by multiplying the two maps and thresholding with a threshold value of 0.5.
  • a median filter can be used to regularize the result. Such a process can be used to invalidate pixels that are outside a predetermined range.
  • method 110 may include enhancing edges between adjacent pixel regions with different depth values in the native depth image.
  • a time of flight depth camera tends to blur the edges, since the depth value as calculated in this technology is an average of the depth in the pixel field of view.
  • a structured light depth camera's depth on edges is typically not measured and the data is synthesized, creating sharp transitions between objects.
  • the native depth image from the time of flight depth camera may be processed to enhance edges between adjacent pixel regions with different depth values to make the edges sharper. Any suitable approach may be used for such edge enhancement, and one such suitable approach is described in detail as follows.
  • method 110 may include quantizing depth values.
  • depth values may be quantized to emulate a depth image received from a structured light depth camera.
  • Structured light technology is based on triangulation to compute the depth.
  • the depth is a function of the pattern displacement, which is quantized as the native sensor resolution is finite.
  • the depth measurements are not related to the native resolution.
  • T ( D ) INT(focal_length*(camera_illum/ D +TAN(angle))/pixel_size+0.5), where focal_length is the focal length of the structured light depth camera, pixel_size is the sensor pixel size, camera_illum is the distance between the camera sensor and illumination centers, and angle is the angle of the object with respect to the line perpendicular from the camera sensor center.
  • the depth map has quantized depth in a similar manner to that created by triangulation computations in the structured light technology.
  • method 110 may include assigning deeper depth values to pixels belonging to objects below a threshold size.
  • deeper depth values may be assigned to pixels of small objects to emulate a depth image received from a structured light depth camera.
  • a structured light depth camera is based on patterns which cannot be projected well on objects that are too small. Thus, such small objects are often assigned the background depth values.
  • Deeper depth values may be assigned to pixels of small objects in any suitable manner.
  • One such approach includes performing a morphological closing. As such, the structuring element depends on the resolution. As an example, a ball element with an approximate 3 ⁇ 3 size may be used. Thus, the smallest object size that can be observed in the depth image can be corrected and the effects are similar to the post-processing done in structured light depth cameras.
  • method 110 may include assigning shadow pixel values to pixels that are virtually occluded from a virtual vantage point of a virtual illuminator virtually spaced away from the time of flight depth camera.
  • shadow pixel values may be assigned to emulate a depth image received from a structured light depth camera.
  • a structured light depth camera works on the principal of triangulation. Due to this fact, the light source is distanced from the sensor and a “shadow” effect is created on the sensor of the camera. Thus, “shadowed pixels” are pixels that are visible to the sensor but not directly visible from the position of the light source, as illustrated in FIG. 7 .
  • a first object 140 occludes a second object 142 from receiving direct light from light emitter 144 .
  • sensor 150 also received shadowed pixels 152 of second object 142 .
  • this “shadowing” artifact in the native depth image received from the time of flight depth camera. It can be appreciated that a shadow effect already occurs in a time of flight depth camera, due to a small distance between the sensor and surrounding emitters; however, this may be normalized in the camera and therefore it may not be visible in the produced depth video.
  • An example solution for synthesizing the shadows uses a lightweight algorithm.
  • the algorithm creates a virtual camera in the same place as where the light emitter would be in the modeled depth camera.
  • the algorithm then transforms the depth samples from the original sensor to this virtual sensor. This transformation can be done using the following equations:
  • fl x imgWidthPix 2 ⁇ ⁇ TAN ⁇ ( FOV x )
  • X world Z world fl x ⁇ ( X sensorImage - principalPt x )
  • X emitterImage fl x ⁇ ( X world + distanceToEmitter ) Z world + 1 2 ⁇ imgWidthPix .
  • FIG. 8 illustrates the idea where the shadowed pixels 152 are considered as shadowed due to the drop in the values of X world .
  • a morphological “open” filtering can be applied to the map of “shadowed pixels.” This step removes small shadows and makes the edges appear more square-like, thus emulating edges from structured light depth cameras.
  • method 110 includes outputting the emulation depth image having the supported type.
  • Such an emulation depth image may be used by a depth image consumer that cannot process native depth images from the depth camera.
  • an application designed to process structured light depth images from a structured light depth camera may receive and process emulation depth images based off of native depth images measured using a time of flight depth camera.
  • FIG. 9 is a high level implementation diagram showing an example image processing block 160 within a depth abstraction layer.
  • FIG. 10 shows the image processing block 160 of FIG. 9 in more detail.
  • image processing block 160 may receive a raw depth map, information relating to the IR illumination image, and depth camera parameters as inputs.
  • the image processing block may output an abstracted emulation depth image 162 that can be used by a depth image consumer, as described above.
  • FIG. 1 a variety of different computing systems may be used without departing from the spirit of this disclosure.
  • the operating environment described with reference to FIG. 1 is provided as an example, but is not meant to be limiting in any way. To the contrary, the illustrated operating environment is intended to demonstrate a general concept, which may be applied to a variety of different operating environments without departing from the scope of this disclosure.
  • the schematic depictions of the depth camera interchangeability systems illustrated in FIG. 2 provide a simplified framework for describing depth image virtualization and depth image emulation, but are not intended to limit the application to only those configurations shown in the drawings. To the contrary, the methods and processes described herein may be tied to a variety of different types of computing systems.
  • FIG. 1 shows a nonlimiting example computing system that may be configured to implement the depth camera interchangeability system with a gaming console 12 and a depth camera 20 .
  • FIG. 11 schematically shows a computing system 170 that may perform depth image abstraction and/or depth image emulation, as described herein.
  • Computing system 170 may take a variety of different forms, including, but not limited to, gaming consoles, personal computing systems, military tracking and/or targeting systems, and character acquisition systems offering green-screen or motion-capture functionality, among others.
  • Computing system 170 may include a logic subsystem 172 , a data-holding subsystem 174 operatively connected to the logic subsystem, a display subsystem 176 , and/or a depth image producer 178 .
  • the computing system may optionally include components not shown in FIG. 11 , and/or some components shown in FIG. 11 may be peripheral components that are not integrated into the computing system.
  • Logic subsystem 172 may include one or more physical devices configured to execute one or more instructions.
  • the logic subsystem may be configured to execute one or more instructions that are part of one or more programs, routines, objects, components, data structures, application programming interfaces, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more devices, communicate information to and/or from different computing objects, or otherwise arrive at a desired result.
  • the logic subsystem may include one or more processors that are configured to execute software instructions. Additionally or alternatively, the logic subsystem may include one or more hardware or firmware logic machines configured to execute hardware or firmware instructions.
  • the logic subsystem may optionally include individual components that are distributed throughout two or more devices, which may be remotely located in some embodiments.
  • Data-holding subsystem 174 may include one or more physical, non-transitory, devices configured to hold data and/or instructions executable by the logic subsystem to implement the herein described methods and processes. When such methods and processes are implemented, the state of data-holding subsystem 174 may be transformed (e.g., to hold different data). Data-holding subsystem 174 may include removable media and/or built-in devices. Data-holding subsystem 174 may include optical memory devices, semiconductor memory devices (e.g., RAM, EEPROM, flash, etc.), and/or magnetic memory devices, among others.
  • Data-holding subsystem 174 may include devices with one or more of the following characteristics: volatile, nonvolatile, dynamic, static, read/write, read-only, random access, sequential access, location addressable, file addressable, and content addressable.
  • logic subsystem 172 and data-holding subsystem 174 may be integrated into one or more common devices, such as an application specific integrated circuit or a system on a chip.
  • module and engine may be used to describe an aspect of computing system 170 that is implemented to perform one or more particular functions.
  • a module or engine may be instantiated via logic subsystem 172 executing instructions held by data-holding subsystem 174 .
  • different modules and/or engines may be instantiated from the same application, code block, object, routine, and/or function.
  • the same module and/or engine may be instantiated by different applications, code blocks, objects, routines, and/or functions in some cases.
  • one or more of the modules described with reference to FIG. 2 may be implemented as an API.
  • Display subsystem 176 may be used to present a visual representation of data held by data-holding subsystem 174 . As the herein described methods and processes change the data held by the data-holding subsystem, and thus transform the state of the data-holding subsystem, the state of display subsystem 176 may likewise be transformed to visually represent changes in the underlying data.
  • Display subsystem 176 may include one or more display devices utilizing virtually any type of technology. Such display devices may be combined with logic subsystem 172 and/or data-holding subsystem 174 in a shared enclosure, or such display devices may be peripheral display devices, as shown in FIG. 1 .
  • Computing system 170 further includes a depth image producer 178 configured to obtain depth images of one or more targets and/or scenes.
  • Depth image producer 178 may be configured to capture video with depth information via any suitable technique (e.g., time-of flight, structured light, stereo image, etc.).
  • depth image producer 178 may include a depth camera, a video camera, stereo cameras, and/or other suitable capture devices.
  • a depth image producer may include one or more on-camera processors and/or off-camera processors to translate raw depth camera data into depth images.
  • a depth camera may optionally include one or more onboard processing units configured to perform one or more depth analysis functions.
  • a depth camera may include firmware to facilitate updating such onboard processing logic.
  • the depth image producer 178 may include a time of flight camera configured to emit infrared light to the scene and may then use sensors to detect the backscattered light from the surfaces of the scene.
  • pulsed infrared light may be used, wherein the time between an outgoing light pulse and a corresponding incoming light pulse may be measured and used to determine a physical distance from the capture device to a particular location on the scene.
  • the phase of the outgoing light wave may be compared to the phase of the incoming light wave to determine a phase shift, and the phase shift may be used to determine a physical distance from the capture device to a particular location in the scene.
  • time-of-flight analysis may be used to indirectly determine a physical distance from the capture device to a particular location in the scene by analyzing the intensity of the reflected beam of light over time via a technique such as shuttered light pulse imaging.
  • structured light analysis may be utilized by depth image producer 178 to capture depth information.
  • patterned light i.e., light displayed as a known pattern such as a grid pattern or a stripe pattern
  • the pattern may become deformed, and this deformation of the pattern may be studied to determine a physical distance from the capture device to a particular location in the scene.
  • the capture device may include two or more physically separated cameras that view a scene from different angles to obtain visual stereo data.
  • the visual stereo data may be resolved to generate a depth image.
  • depth image producer 178 may utilize other technologies to measure and/or calculate depth values.
  • two or more different cameras may be incorporated as part of a depth image producer.
  • a depth camera and a video camera e.g., RGB video camera
  • RGB video camera e.g., RGB video camera
  • a video camera it may be used to provide target tracking data, confirmation data for error correction of scene analysis, image capture, face recognition, high-precision tracking of fingers (or other small features), light sensing, and/or other functions.
  • the above described compatibility technologies may be used to use/emulate two or more depth cameras at the same time.
  • two cameras may be used to view adjacent scenes, and the API may effectively combine information from both cameras to emulate a single camera with a wider field of view.
  • a single, wide angle camera may be used, and the API may produce two separate depth images as if generated by two cameras with narrow fields of view looking in different directions.
  • two or more depth cameras may be used to look at the same scene from different vantage points.
  • the API may effectively combine information from both cameras to provide more/better 3D data in a way that is transparent to the depth image consumer.

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EP11740159.6A EP2531979B1 (de) 2010-02-02 2011-01-10 Tiefenkamera-kompatibilität
CN201180007815.7A CN102763420B (zh) 2010-02-02 2011-01-10 深度相机兼容性
KR1020127020257A KR101783379B1 (ko) 2010-02-02 2011-01-10 깊이 카메라 호환성
CA2786436A CA2786436C (en) 2010-02-02 2011-01-10 Depth camera compatibility
PCT/US2011/020690 WO2011097049A2 (en) 2010-02-02 2011-01-10 Depth camera compatibility
JP2012551984A JP5778182B2 (ja) 2010-02-02 2011-01-10 デプスカメラの互換性
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130202161A1 (en) * 2012-02-05 2013-08-08 Primesense Ltd. Enhanced face detection using depth information
US20140282278A1 (en) * 2013-03-14 2014-09-18 Glen J. Anderson Depth-based user interface gesture control
US10386468B2 (en) 2015-03-05 2019-08-20 Hanwha Techwin Co., Ltd. Photographing apparatus and method

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110292036A1 (en) * 2010-05-31 2011-12-01 Primesense Ltd. Depth sensor with application interface
US8582867B2 (en) 2010-09-16 2013-11-12 Primesense Ltd Learning-based pose estimation from depth maps
US8654219B2 (en) * 2011-04-26 2014-02-18 Lg Electronics Inc. Method and apparatus for restoring dead pixel using light intensity map in a time-of-flight camera
US11048333B2 (en) 2011-06-23 2021-06-29 Intel Corporation System and method for close-range movement tracking
US9002099B2 (en) 2011-09-11 2015-04-07 Apple Inc. Learning-based estimation of hand and finger pose
US10366299B2 (en) * 2011-10-25 2019-07-30 Bull Hn Information Systems, Inc. Sorting/scanning system camera upgrade apparatus with backwards compatibility
US8660362B2 (en) 2011-11-21 2014-02-25 Microsoft Corporation Combined depth filtering and super resolution
US20140104394A1 (en) * 2012-10-15 2014-04-17 Intel Corporation System and method for combining data from multiple depth cameras
CN103543830B (zh) * 2013-10-28 2017-02-15 四川大学 一种人体骨骼点映射至立体显示中虚拟三维空间点的方法
CN106575158B (zh) * 2014-09-08 2020-08-21 英特尔公司 环境映射虚拟化机构
DE112015005020T5 (de) * 2014-11-05 2017-07-27 Trw Automotive U.S. Llc Verbesserte objektdetektion unter verwendung von strukturiertem licht
CN106709865B (zh) * 2015-11-13 2020-02-18 杭州海康威视数字技术股份有限公司 一种深度图像合成方法及装置
KR101893368B1 (ko) * 2018-01-31 2018-09-04 공간정보기술 주식회사 교통 모니터링 카메라를 이용하는 3차원 시정 측정 방법 및 시스템
US11393115B2 (en) * 2018-11-27 2022-07-19 Infineon Technologies Ag Filtering continuous-wave time-of-flight measurements, based on coded modulation images
CN110276791B (zh) * 2019-06-28 2021-04-06 北京航空航天大学 一种参数可配置的深度相机仿真方法
CN116256769A (zh) * 2021-12-10 2023-06-13 浙江舜宇智能光学技术有限公司 iToF深度计算装置、方法、iToF相机模组和系统

Citations (191)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4627620A (en) 1984-12-26 1986-12-09 Yang John P Electronic athlete trainer for improving skills in reflex, speed and accuracy
US4630910A (en) 1984-02-16 1986-12-23 Robotic Vision Systems, Inc. Method of measuring in three-dimensions at high speed
US4645458A (en) 1985-04-15 1987-02-24 Harald Phillip Athletic evaluation and training apparatus
US4695953A (en) 1983-08-25 1987-09-22 Blair Preston E TV animation interactively controlled by the viewer
US4702475A (en) 1985-08-16 1987-10-27 Innovating Training Products, Inc. Sports technique and reaction training system
US4711543A (en) 1986-04-14 1987-12-08 Blair Preston E TV animation interactively controlled by the viewer
US4751642A (en) 1986-08-29 1988-06-14 Silva John M Interactive sports simulation system with physiological sensing and psychological conditioning
US4796997A (en) 1986-05-27 1989-01-10 Synthetic Vision Systems, Inc. Method and system for high-speed, 3-D imaging of an object at a vision station
US4809065A (en) 1986-12-01 1989-02-28 Kabushiki Kaisha Toshiba Interactive system and related method for displaying data to produce a three-dimensional image of an object
US4817950A (en) 1987-05-08 1989-04-04 Goo Paul E Video game control unit and attitude sensor
US4843568A (en) 1986-04-11 1989-06-27 Krueger Myron W Real time perception of and response to the actions of an unencumbered participant/user
US4893183A (en) 1988-08-11 1990-01-09 Carnegie-Mellon University Robotic vision system
US4901362A (en) 1988-08-08 1990-02-13 Raytheon Company Method of recognizing patterns
US4925189A (en) 1989-01-13 1990-05-15 Braeunig Thomas F Body-mounted video game exercise device
US5101444A (en) 1990-05-18 1992-03-31 Panacea, Inc. Method and apparatus for high speed object location
US5148154A (en) 1990-12-04 1992-09-15 Sony Corporation Of America Multi-dimensional user interface
US5184295A (en) 1986-05-30 1993-02-02 Mann Ralph V System and method for teaching physical skills
WO1993010708A1 (en) 1991-12-03 1993-06-10 French Sportech Corporation Interactive video testing and training system
US5229756A (en) 1989-02-07 1993-07-20 Yamaha Corporation Image control apparatus
US5229754A (en) 1990-02-13 1993-07-20 Yazaki Corporation Automotive reflection type display apparatus
US5239464A (en) 1988-08-04 1993-08-24 Blair Preston E Interactive video system providing repeated switching of multiple tracks of actions sequences
US5239463A (en) 1988-08-04 1993-08-24 Blair Preston E Method and apparatus for player interaction with animated characters and objects
EP0583061A2 (de) 1992-07-10 1994-02-16 The Walt Disney Company Verfahren und Gerät zum Erzeugen von verbesserte Graphiken in einer virtuallen Welt
US5288078A (en) 1988-10-14 1994-02-22 David G. Capper Control interface apparatus
US5295491A (en) 1991-09-26 1994-03-22 Sam Technology, Inc. Non-invasive human neurocognitive performance capability testing method and system
US5320538A (en) 1992-09-23 1994-06-14 Hughes Training, Inc. Interactive aircraft training system and method
US5347306A (en) 1993-12-17 1994-09-13 Mitsubishi Electric Research Laboratories, Inc. Animated electronic meeting place
US5385519A (en) 1994-04-19 1995-01-31 Hsu; Chi-Hsueh Running machine
US5405152A (en) 1993-06-08 1995-04-11 The Walt Disney Company Method and apparatus for an interactive video game with physical feedback
US5417210A (en) 1992-05-27 1995-05-23 International Business Machines Corporation System and method for augmentation of endoscopic surgery
US5423554A (en) 1993-09-24 1995-06-13 Metamedia Ventures, Inc. Virtual reality game method and apparatus
US5454043A (en) 1993-07-30 1995-09-26 Mitsubishi Electric Research Laboratories, Inc. Dynamic and static hand gesture recognition through low-level image analysis
US5469740A (en) 1989-07-14 1995-11-28 Impulse Technology, Inc. Interactive video testing and training system
US5495576A (en) 1993-01-11 1996-02-27 Ritchey; Kurtis J. Panoramic image based virtual reality/telepresence audio-visual system and method
US5516105A (en) 1994-10-06 1996-05-14 Exergame, Inc. Acceleration activated joystick
US5524637A (en) 1994-06-29 1996-06-11 Erickson; Jon W. Interactive system for measuring physiological exertion
US5534917A (en) 1991-05-09 1996-07-09 Very Vivid, Inc. Video image based control system
US5563988A (en) 1994-08-01 1996-10-08 Massachusetts Institute Of Technology Method and system for facilitating wireless, full-body, real-time user interaction with a digitally represented visual environment
US5577981A (en) 1994-01-19 1996-11-26 Jarvik; Robert Virtual reality exercise machine and computer controlled video system
US5580249A (en) 1994-02-14 1996-12-03 Sarcos Group Apparatus for simulating mobility of a human
US5594469A (en) 1995-02-21 1997-01-14 Mitsubishi Electric Information Technology Center America Inc. Hand gesture machine control system
US5597309A (en) 1994-03-28 1997-01-28 Riess; Thomas Method and apparatus for treatment of gait problems associated with parkinson's disease
US5617312A (en) 1993-11-19 1997-04-01 Hitachi, Ltd. Computer system that enters control information by means of video camera
US5616078A (en) 1993-12-28 1997-04-01 Konami Co., Ltd. Motion-controlled video entertainment system
WO1997017598A1 (en) 1995-11-06 1997-05-15 Impulse Technology, Inc. System for continuous monitoring of physical activity during unrestricted movement
US5638300A (en) 1994-12-05 1997-06-10 Johnson; Lee E. Golf swing analysis system
US5641288A (en) 1996-01-11 1997-06-24 Zaenglein, Jr.; William G. Shooting simulating process and training device using a virtual reality display screen
US5682196A (en) 1995-06-22 1997-10-28 Actv, Inc. Three-dimensional (3D) video presentation system providing interactive 3D presentation with personalized audio responses for multiple viewers
US5682229A (en) 1995-04-14 1997-10-28 Schwartz Electro-Optics, Inc. Laser range camera
US5690582A (en) 1993-02-02 1997-11-25 Tectrix Fitness Equipment, Inc. Interactive exercise apparatus
US5703367A (en) 1994-12-09 1997-12-30 Matsushita Electric Industrial Co., Ltd. Human occupancy detection method and system for implementing the same
US5704837A (en) 1993-03-26 1998-01-06 Namco Ltd. Video game steering system causing translation, rotation and curvilinear motion on the object
US5715834A (en) 1992-11-20 1998-02-10 Scuola Superiore Di Studi Universitari & Di Perfezionamento S. Anna Device for monitoring the configuration of a distal physiological unit for use, in particular, as an advanced interface for machine and computers
US5875108A (en) 1991-12-23 1999-02-23 Hoffberg; Steven M. Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US5877803A (en) 1997-04-07 1999-03-02 Tritech Mircoelectronics International, Ltd. 3-D image detector
US5913727A (en) 1995-06-02 1999-06-22 Ahdoot; Ned Interactive movement and contact simulation game
US5933125A (en) 1995-11-27 1999-08-03 Cae Electronics, Ltd. Method and apparatus for reducing instability in the display of a virtual environment
US5949430A (en) 1997-05-20 1999-09-07 Microsoft Corporation Peripheral lenses for simulating peripheral vision on a display device
US5980256A (en) 1993-10-29 1999-11-09 Carmein; David E. E. Virtual reality system with enhanced sensory apparatus
US5989157A (en) 1996-08-06 1999-11-23 Walton; Charles A. Exercising system with electronic inertial game playing
US5995649A (en) 1996-09-20 1999-11-30 Nec Corporation Dual-input image processor for recognizing, isolating, and displaying specific objects from the input images
US6005548A (en) 1996-08-14 1999-12-21 Latypov; Nurakhmed Nurislamovich Method for tracking and displaying user's spatial position and orientation, a method for representing virtual reality for a user, and systems of embodiment of such methods
US6009210A (en) 1997-03-05 1999-12-28 Digital Equipment Corporation Hands-free interface to a virtual reality environment using head tracking
US6054991A (en) 1991-12-02 2000-04-25 Texas Instruments Incorporated Method of modeling player position and movement in a virtual reality system
US6066075A (en) 1995-07-26 2000-05-23 Poulton; Craig K. Direct feedback controller for user interaction
US6072494A (en) 1997-10-15 2000-06-06 Electric Planet, Inc. Method and apparatus for real-time gesture recognition
US6073489A (en) 1995-11-06 2000-06-13 French; Barry J. Testing and training system for assessing the ability of a player to complete a task
US6077201A (en) 1998-06-12 2000-06-20 Cheng; Chau-Yang Exercise bicycle
US6101289A (en) 1997-10-15 2000-08-08 Electric Planet, Inc. Method and apparatus for unencumbered capture of an object
US6100896A (en) 1997-03-24 2000-08-08 Mitsubishi Electric Information Technology Center America, Inc. System for designing graphical multi-participant environments
US6111582A (en) 1996-12-20 2000-08-29 Jenkins; Barry L. System and method of image generation and encoding using primitive reprojection
US6124864A (en) 1997-04-07 2000-09-26 Synapix, Inc. Adaptive modeling and segmentation of visual image streams
US6128003A (en) 1996-12-20 2000-10-03 Hitachi, Ltd. Hand gesture recognition system and method
US6130677A (en) 1997-10-15 2000-10-10 Electric Planet, Inc. Interactive computer vision system
US6141463A (en) 1997-10-10 2000-10-31 Electric Planet Interactive Method and system for estimating jointed-figure configurations
US6147678A (en) 1998-12-09 2000-11-14 Lucent Technologies Inc. Video hand image-three-dimensional computer interface with multiple degrees of freedom
US6152856A (en) 1996-05-08 2000-11-28 Real Vision Corporation Real time simulation using position sensing
US6159100A (en) 1998-04-23 2000-12-12 Smith; Michael D. Virtual reality game
US6173066B1 (en) 1996-05-21 2001-01-09 Cybernet Systems Corporation Pose determination and tracking by matching 3D objects to a 2D sensor
US6181343B1 (en) 1997-12-23 2001-01-30 Philips Electronics North America Corp. System and method for permitting three-dimensional navigation through a virtual reality environment using camera-based gesture inputs
US6188777B1 (en) 1997-08-01 2001-02-13 Interval Research Corporation Method and apparatus for personnel detection and tracking
US6215898B1 (en) 1997-04-15 2001-04-10 Interval Research Corporation Data processing system and method
US6215890B1 (en) 1997-09-26 2001-04-10 Matsushita Electric Industrial Co., Ltd. Hand gesture recognizing device
US6226396B1 (en) 1997-07-31 2001-05-01 Nec Corporation Object extraction method and system
US6229913B1 (en) 1995-06-07 2001-05-08 The Trustees Of Columbia University In The City Of New York Apparatus and methods for determining the three-dimensional shape of an object using active illumination and relative blurring in two-images due to defocus
US6256400B1 (en) 1998-09-28 2001-07-03 Matsushita Electric Industrial Co., Ltd. Method and device for segmenting hand gestures
US6283860B1 (en) 1995-11-07 2001-09-04 Philips Electronics North America Corp. Method, system, and program for gesture based option selection
US6289112B1 (en) 1997-08-22 2001-09-11 International Business Machines Corporation System and method for determining block direction in fingerprint images
US6299308B1 (en) 1999-04-02 2001-10-09 Cybernet Systems Corporation Low-cost non-imaging eye tracker system for computer control
US6308565B1 (en) 1995-11-06 2001-10-30 Impulse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
US6316934B1 (en) 1998-09-17 2001-11-13 Netmor Ltd. System for three dimensional positioning and tracking
US6327097B1 (en) 1997-04-17 2001-12-04 Zbig Vision Gesellschaft Fur Neue Bildgestaltung Mbh Optical imaging system and graphic user interface
US6363160B1 (en) 1999-01-22 2002-03-26 Intel Corporation Interface using pattern recognition and tracking
US6373489B1 (en) 1999-01-12 2002-04-16 Schlumberger Technology Corporation Scalable visualization for interactive geometry modeling
US6384819B1 (en) 1997-10-15 2002-05-07 Electric Planet, Inc. System and method for generating an animatable character
US6411744B1 (en) 1997-10-15 2002-06-25 Electric Planet, Inc. Method and apparatus for performing a clean background subtraction
US6430997B1 (en) 1995-11-06 2002-08-13 Trazer Technologies, Inc. System and method for tracking and assessing movement skills in multidimensional space
US6476834B1 (en) 1999-05-28 2002-11-05 International Business Machines Corporation Dynamic creation of selectable items on surfaces
US6496598B1 (en) 1997-09-02 2002-12-17 Dynamic Digital Depth Research Pty. Ltd. Image processing method and apparatus
US6503195B1 (en) 1999-05-24 2003-01-07 University Of North Carolina At Chapel Hill Methods and systems for real-time structured light depth extraction and endoscope using real-time structured light depth extraction
US6539931B2 (en) 2001-04-16 2003-04-01 Koninklijke Philips Electronics N.V. Ball throwing assistant
US6570555B1 (en) 1998-12-30 2003-05-27 Fuji Xerox Co., Ltd. Method and apparatus for embodied conversational characters with multimodal input/output in an interface device
US6633294B1 (en) 2000-03-09 2003-10-14 Seth Rosenthal Method and apparatus for using captured high density motion for animation
US6640202B1 (en) 2000-05-25 2003-10-28 International Business Machines Corporation Elastic sensor mesh system for 3-dimensional measurement, mapping and kinematics applications
US6661918B1 (en) 1998-12-04 2003-12-09 Interval Research Corporation Background estimation and segmentation based on range and color
US6681031B2 (en) 1998-08-10 2004-01-20 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US6714665B1 (en) 1994-09-02 2004-03-30 Sarnoff Corporation Fully automated iris recognition system utilizing wide and narrow fields of view
US6731799B1 (en) 2000-06-01 2004-05-04 University Of Washington Object segmentation with background extraction and moving boundary techniques
US6738066B1 (en) 1999-07-30 2004-05-18 Electric Plant, Inc. System, method and article of manufacture for detecting collisions between video images generated by a camera and an object depicted on a display
US20040109585A1 (en) * 2002-12-09 2004-06-10 Hai Tao Dynamic depth recovery from multiple synchronized video streams
US6788809B1 (en) 2000-06-30 2004-09-07 Intel Corporation System and method for gesture recognition in three dimensions using stereo imaging and color vision
US6798406B1 (en) 1999-09-15 2004-09-28 Sharp Kabushiki Kaisha Stereo images with comfortable perceived depth
US6801637B2 (en) 1999-08-10 2004-10-05 Cybernet Systems Corporation Optical body tracker
US6873723B1 (en) 1999-06-30 2005-03-29 Intel Corporation Segmenting three-dimensional video images using stereo
US20050089213A1 (en) * 2003-10-23 2005-04-28 Geng Z. J. Method and apparatus for three-dimensional modeling via an image mosaic system
JP2005117475A (ja) 2003-10-09 2005-04-28 Canon Inc カメラサーバの色補正方法
US6937742B2 (en) 2001-09-28 2005-08-30 Bellsouth Intellectual Property Corporation Gesture activated home appliance
US6950534B2 (en) 1998-08-10 2005-09-27 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US7003134B1 (en) 1999-03-08 2006-02-21 Vulcan Patents Llc Three dimensional object pose estimation which employs dense depth information
US7036094B1 (en) 1998-08-10 2006-04-25 Cybernet Systems Corporation Behavior recognition system
US7039676B1 (en) 2000-10-31 2006-05-02 International Business Machines Corporation Using video image analysis to automatically transmit gestures over a network in a chat or instant messaging session
US7042440B2 (en) 1997-08-22 2006-05-09 Pryor Timothy R Man machine interfaces and applications
US7050606B2 (en) 1999-08-10 2006-05-23 Cybernet Systems Corporation Tracking and gesture recognition system particularly suited to vehicular control applications
US7058204B2 (en) 2000-10-03 2006-06-06 Gesturetek, Inc. Multiple camera control system
US7060957B2 (en) 2000-04-28 2006-06-13 Csem Centre Suisse D'electronique Et Microtechinique Sa Device and method for spatially resolved photodetection and demodulation of modulated electromagnetic waves
US7113918B1 (en) 1999-08-01 2006-09-26 Electric Planet, Inc. Method for video enabled electronic commerce
US7121946B2 (en) 1998-08-10 2006-10-17 Cybernet Systems Corporation Real-time head tracking system for computer games and other applications
US7170492B2 (en) 2002-05-28 2007-01-30 Reactrix Systems, Inc. Interactive video display system
US7202898B1 (en) 1998-12-16 2007-04-10 3Dv Systems Ltd. Self gating photosurface
US20070109409A1 (en) * 2004-12-17 2007-05-17 Sehoon Yea Method and System for Processing Multiview Videos for View Synthesis using Skip and Direct Modes
US7222078B2 (en) 1992-08-06 2007-05-22 Ferrara Ethereal Llc Methods and systems for gathering information from units of a commodity across a network
US7227526B2 (en) 2000-07-24 2007-06-05 Gesturetek, Inc. Video-based image control system
US7259747B2 (en) 2001-06-05 2007-08-21 Reactrix Systems, Inc. Interactive video display system
US20070217682A1 (en) * 2006-03-15 2007-09-20 Matsushita Electric Industrial Co., Ltd. Method and apparatus for image conversion
US20070236510A1 (en) 2006-04-06 2007-10-11 Hiroyuki Kakuta Image processing apparatus, control method thereof, and program
US20070248350A1 (en) 2006-04-25 2007-10-25 Baker Henry H Automated process for generating a computed design of a composite camera comprising multiple digital imaging devices
US20070247522A1 (en) 2003-12-18 2007-10-25 University Of Durham Method and Apparatus for Generating a Stereoscopic Image
US20070279415A1 (en) 2006-06-01 2007-12-06 Steve Sullivan 2D to 3D image conversion
US7308112B2 (en) 2004-05-14 2007-12-11 Honda Motor Co., Ltd. Sign based human-machine interaction
WO2007148219A2 (en) * 2006-06-23 2007-12-27 Imax Corporation Methods and systems for converting 2d motion pictures for stereoscopic 3d exhibition
US7317836B2 (en) 2005-03-17 2008-01-08 Honda Motor Co., Ltd. Pose estimation based on critical point analysis
US20080026838A1 (en) 2005-08-22 2008-01-31 Dunstan James E Multi-player non-role-playing virtual world games: method for two-way interaction between participants and multi-player virtual world games
WO2008020734A1 (en) * 2006-08-18 2008-02-21 Gwangju Institute Of Science And Technology A method and apparatus for encoding or decoding frames of different views in multiview video using global disparity
US7348963B2 (en) 2002-05-28 2008-03-25 Reactrix Systems, Inc. Interactive video display system
US7367887B2 (en) 2000-02-18 2008-05-06 Namco Bandai Games Inc. Game apparatus, storage medium, and computer program that adjust level of game difficulty
US20080112610A1 (en) * 2006-11-14 2008-05-15 S2, Inc. System and method for 3d model generation
US7379563B2 (en) 2004-04-15 2008-05-27 Gesturetek, Inc. Tracking bimanual movements
US7379566B2 (en) 2005-01-07 2008-05-27 Gesturetek, Inc. Optical flow based tilt sensor
US7389591B2 (en) 2005-05-17 2008-06-24 Gesturetek, Inc. Orientation-sensitive signal output
US7412077B2 (en) 2006-12-29 2008-08-12 Motorola, Inc. Apparatus and methods for head pose estimation and head gesture detection
US7430312B2 (en) 2005-01-07 2008-09-30 Gesturetek, Inc. Creating 3D images of objects by illuminating with infrared patterns
US20080246757A1 (en) * 2005-04-25 2008-10-09 Masahiro Ito 3D Image Generation and Display System
US20080246759A1 (en) * 2005-02-23 2008-10-09 Craig Summers Automatic Scene Modeling for the 3D Camera and 3D Video
US7436496B2 (en) 2003-02-03 2008-10-14 National University Corporation Shizuoka University Distance image sensor
US20080252661A1 (en) 2005-09-27 2008-10-16 John Allen Hilton Interface for Computer Controllers
US7450736B2 (en) 2005-10-28 2008-11-11 Honda Motor Co., Ltd. Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers
US7452275B2 (en) 2001-06-29 2008-11-18 Konami Digital Entertainment Co., Ltd. Game device, game controlling method and program
US20090002483A1 (en) * 2004-03-02 2009-01-01 Kabushiki Kaisha Toshiba Apparatus for and method of generating image, and computer program product
US7489812B2 (en) 2002-06-07 2009-02-10 Dynamic Digital Depth Research Pty Ltd. Conversion and encoding techniques
US7536032B2 (en) 2003-10-24 2009-05-19 Reactrix Systems, Inc. Method and system for processing captured image information in an interactive video display system
WO2009069958A2 (en) 2007-11-29 2009-06-04 Gwangju Institute Of Science And Technology Method and apparatus for generating multi-viewpoint depth map, method for generating disparity of multi-viewpoint image
CN201254344Y (zh) 2008-08-20 2009-06-10 中国农业科学院草原研究所 植物标本及种子存贮器
US20090158220A1 (en) * 2007-12-17 2009-06-18 Sony Computer Entertainment America Dynamic three-dimensional object mapping for user-defined control device
US7551771B2 (en) * 2005-09-20 2009-06-23 Deltasphere, Inc. Methods, systems, and computer program products for acquiring three-dimensional range information
US20090160934A1 (en) * 2007-07-23 2009-06-25 Disney Enterprises, Inc. Generation of three-dimensional movies with improved depth control
US20090169057A1 (en) * 2007-12-28 2009-07-02 Industrial Technology Research Institute Method for producing image with depth by using 2d images
US7560701B2 (en) 2005-08-12 2009-07-14 Mesa Imaging Ag Highly sensitive, fast pixel for use in an image sensor
EP2081376A1 (de) 2006-11-10 2009-07-22 Mitsubishi Electric Corporation Netzwerkbildsynthetisierendes anzeigesystem
US7574020B2 (en) 2005-01-07 2009-08-11 Gesturetek, Inc. Detecting and tracking objects in images
US7576727B2 (en) 2002-12-13 2009-08-18 Matthew Bell Interactive directed light/sound system
US7590262B2 (en) * 2003-05-29 2009-09-15 Honda Motor Co., Ltd. Visual tracking using depth data
US20090232389A1 (en) * 2008-03-12 2009-09-17 Samsung Electronics Co., Ltd. Image processing method and apparatus, image reproducing method and apparatus, and recording medium
US20090231425A1 (en) 2008-03-17 2009-09-17 Sony Computer Entertainment America Controller with an integrated camera and methods for interfacing with an interactive application
US7593552B2 (en) 2003-03-31 2009-09-22 Honda Motor Co., Ltd. Gesture recognition apparatus, gesture recognition method, and gesture recognition program
US7598942B2 (en) 2005-02-08 2009-10-06 Oblong Industries, Inc. System and method for gesture based control system
US7607509B2 (en) 2002-04-19 2009-10-27 Iee International Electronics & Engineering S.A. Safety device for a vehicle
US7620202B2 (en) 2003-06-12 2009-11-17 Honda Motor Co., Ltd. Target orientation estimation using depth sensing
US20090284654A1 (en) * 2008-05-19 2009-11-19 Kabushiki Kaisha Toshiba Synchronizing signal control circuit and synchronizing signal control method
US20090315982A1 (en) 2006-11-22 2009-12-24 Alexander Schmidt Arrangement and method for the recording and display of images of a scene and/or an object
US7683954B2 (en) 2006-09-29 2010-03-23 Brainvision Inc. Solid-state image sensor
US7684592B2 (en) 1998-08-10 2010-03-23 Cybernet Systems Corporation Realtime object tracking system
US7702130B2 (en) 2004-12-20 2010-04-20 Electronics And Telecommunications Research Institute User interface apparatus using hand gesture recognition and method thereof
US7701439B2 (en) 2006-07-13 2010-04-20 Northrop Grumman Corporation Gesture recognition simulation system and method
US7704135B2 (en) 2004-08-23 2010-04-27 Harrison Jr Shelton E Integrated game system, method, and device
US7710391B2 (en) 2002-05-28 2010-05-04 Matthew Bell Processing an image utilizing a spatially varying pattern
US7729530B2 (en) 2007-03-03 2010-06-01 Sergey Antonov Method and apparatus for 3-D data input to a personal computer with a multimedia oriented operating system
US7835569B2 (en) * 2006-10-13 2010-11-16 Apple Inc. System and method for raw image processing using conversion matrix interpolated from predetermined camera characterization matrices
US7852262B2 (en) 2007-08-16 2010-12-14 Cybernet Systems Corporation Wireless mobile indoor/outdoor tracking system
US8009871B2 (en) * 2005-02-08 2011-08-30 Microsoft Corporation Method and system to segment depth images and to detect shapes in three-dimensionally acquired data
US8035612B2 (en) 2002-05-28 2011-10-11 Intellectual Ventures Holding 67 Llc Self-contained interactive video display system
US8072470B2 (en) 2003-05-29 2011-12-06 Sony Computer Entertainment Inc. System and method for providing a real-time three-dimensional interactive environment

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4288078A (en) * 1979-11-20 1981-09-08 Lugo Julio I Game apparatus
JPH07114636A (ja) * 1993-10-15 1995-05-02 Ricoh Co Ltd 画素密度変換装置
JP3969852B2 (ja) * 1998-06-29 2007-09-05 キヤノン株式会社 移動体間通信システム、通信装置
US20040218269A1 (en) * 2002-01-14 2004-11-04 Divelbiss Adam W. General purpose stereoscopic 3D format conversion system and method
KR100707206B1 (ko) * 2005-04-11 2007-04-13 삼성전자주식회사 3차원 객체의 깊이영상 기반 표현 방법 및 이를 이용한모델링 및 렌더링 방법 및 장치
KR100954281B1 (ko) * 2008-02-14 2010-04-23 한국전자통신연구원 다시점 영상의 엘리애징 제거를 위한 필터링 장치 및 방법
JP4513906B2 (ja) * 2008-06-27 2010-07-28 ソニー株式会社 画像処理装置、画像処理方法、プログラム及び記録媒体
US8619122B2 (en) * 2010-02-02 2013-12-31 Microsoft Corporation Depth camera compatibility

Patent Citations (212)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4695953A (en) 1983-08-25 1987-09-22 Blair Preston E TV animation interactively controlled by the viewer
US4630910A (en) 1984-02-16 1986-12-23 Robotic Vision Systems, Inc. Method of measuring in three-dimensions at high speed
US4627620A (en) 1984-12-26 1986-12-09 Yang John P Electronic athlete trainer for improving skills in reflex, speed and accuracy
US4645458A (en) 1985-04-15 1987-02-24 Harald Phillip Athletic evaluation and training apparatus
US4702475A (en) 1985-08-16 1987-10-27 Innovating Training Products, Inc. Sports technique and reaction training system
US4843568A (en) 1986-04-11 1989-06-27 Krueger Myron W Real time perception of and response to the actions of an unencumbered participant/user
US4711543A (en) 1986-04-14 1987-12-08 Blair Preston E TV animation interactively controlled by the viewer
US4796997A (en) 1986-05-27 1989-01-10 Synthetic Vision Systems, Inc. Method and system for high-speed, 3-D imaging of an object at a vision station
US5184295A (en) 1986-05-30 1993-02-02 Mann Ralph V System and method for teaching physical skills
US4751642A (en) 1986-08-29 1988-06-14 Silva John M Interactive sports simulation system with physiological sensing and psychological conditioning
US4809065A (en) 1986-12-01 1989-02-28 Kabushiki Kaisha Toshiba Interactive system and related method for displaying data to produce a three-dimensional image of an object
US4817950A (en) 1987-05-08 1989-04-04 Goo Paul E Video game control unit and attitude sensor
US5239463A (en) 1988-08-04 1993-08-24 Blair Preston E Method and apparatus for player interaction with animated characters and objects
US5239464A (en) 1988-08-04 1993-08-24 Blair Preston E Interactive video system providing repeated switching of multiple tracks of actions sequences
US4901362A (en) 1988-08-08 1990-02-13 Raytheon Company Method of recognizing patterns
US4893183A (en) 1988-08-11 1990-01-09 Carnegie-Mellon University Robotic vision system
US5288078A (en) 1988-10-14 1994-02-22 David G. Capper Control interface apparatus
US4925189A (en) 1989-01-13 1990-05-15 Braeunig Thomas F Body-mounted video game exercise device
US5229756A (en) 1989-02-07 1993-07-20 Yamaha Corporation Image control apparatus
US5469740A (en) 1989-07-14 1995-11-28 Impulse Technology, Inc. Interactive video testing and training system
US5229754A (en) 1990-02-13 1993-07-20 Yazaki Corporation Automotive reflection type display apparatus
US5101444A (en) 1990-05-18 1992-03-31 Panacea, Inc. Method and apparatus for high speed object location
US5148154A (en) 1990-12-04 1992-09-15 Sony Corporation Of America Multi-dimensional user interface
US5534917A (en) 1991-05-09 1996-07-09 Very Vivid, Inc. Video image based control system
US5295491A (en) 1991-09-26 1994-03-22 Sam Technology, Inc. Non-invasive human neurocognitive performance capability testing method and system
US6054991A (en) 1991-12-02 2000-04-25 Texas Instruments Incorporated Method of modeling player position and movement in a virtual reality system
WO1993010708A1 (en) 1991-12-03 1993-06-10 French Sportech Corporation Interactive video testing and training system
US5875108A (en) 1991-12-23 1999-02-23 Hoffberg; Steven M. Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US5417210A (en) 1992-05-27 1995-05-23 International Business Machines Corporation System and method for augmentation of endoscopic surgery
EP0583061A2 (de) 1992-07-10 1994-02-16 The Walt Disney Company Verfahren und Gerät zum Erzeugen von verbesserte Graphiken in einer virtuallen Welt
US7222078B2 (en) 1992-08-06 2007-05-22 Ferrara Ethereal Llc Methods and systems for gathering information from units of a commodity across a network
US5320538A (en) 1992-09-23 1994-06-14 Hughes Training, Inc. Interactive aircraft training system and method
US5715834A (en) 1992-11-20 1998-02-10 Scuola Superiore Di Studi Universitari & Di Perfezionamento S. Anna Device for monitoring the configuration of a distal physiological unit for use, in particular, as an advanced interface for machine and computers
US5495576A (en) 1993-01-11 1996-02-27 Ritchey; Kurtis J. Panoramic image based virtual reality/telepresence audio-visual system and method
US5690582A (en) 1993-02-02 1997-11-25 Tectrix Fitness Equipment, Inc. Interactive exercise apparatus
US5704837A (en) 1993-03-26 1998-01-06 Namco Ltd. Video game steering system causing translation, rotation and curvilinear motion on the object
US5405152A (en) 1993-06-08 1995-04-11 The Walt Disney Company Method and apparatus for an interactive video game with physical feedback
US5454043A (en) 1993-07-30 1995-09-26 Mitsubishi Electric Research Laboratories, Inc. Dynamic and static hand gesture recognition through low-level image analysis
US5423554A (en) 1993-09-24 1995-06-13 Metamedia Ventures, Inc. Virtual reality game method and apparatus
US5980256A (en) 1993-10-29 1999-11-09 Carmein; David E. E. Virtual reality system with enhanced sensory apparatus
US5617312A (en) 1993-11-19 1997-04-01 Hitachi, Ltd. Computer system that enters control information by means of video camera
US5347306A (en) 1993-12-17 1994-09-13 Mitsubishi Electric Research Laboratories, Inc. Animated electronic meeting place
US5616078A (en) 1993-12-28 1997-04-01 Konami Co., Ltd. Motion-controlled video entertainment system
US5577981A (en) 1994-01-19 1996-11-26 Jarvik; Robert Virtual reality exercise machine and computer controlled video system
US5580249A (en) 1994-02-14 1996-12-03 Sarcos Group Apparatus for simulating mobility of a human
US5597309A (en) 1994-03-28 1997-01-28 Riess; Thomas Method and apparatus for treatment of gait problems associated with parkinson's disease
US5385519A (en) 1994-04-19 1995-01-31 Hsu; Chi-Hsueh Running machine
US5524637A (en) 1994-06-29 1996-06-11 Erickson; Jon W. Interactive system for measuring physiological exertion
US5563988A (en) 1994-08-01 1996-10-08 Massachusetts Institute Of Technology Method and system for facilitating wireless, full-body, real-time user interaction with a digitally represented visual environment
US6714665B1 (en) 1994-09-02 2004-03-30 Sarnoff Corporation Fully automated iris recognition system utilizing wide and narrow fields of view
US5516105A (en) 1994-10-06 1996-05-14 Exergame, Inc. Acceleration activated joystick
US5638300A (en) 1994-12-05 1997-06-10 Johnson; Lee E. Golf swing analysis system
US5703367A (en) 1994-12-09 1997-12-30 Matsushita Electric Industrial Co., Ltd. Human occupancy detection method and system for implementing the same
US5594469A (en) 1995-02-21 1997-01-14 Mitsubishi Electric Information Technology Center America Inc. Hand gesture machine control system
US5682229A (en) 1995-04-14 1997-10-28 Schwartz Electro-Optics, Inc. Laser range camera
US5913727A (en) 1995-06-02 1999-06-22 Ahdoot; Ned Interactive movement and contact simulation game
US6229913B1 (en) 1995-06-07 2001-05-08 The Trustees Of Columbia University In The City Of New York Apparatus and methods for determining the three-dimensional shape of an object using active illumination and relative blurring in two-images due to defocus
US5682196A (en) 1995-06-22 1997-10-28 Actv, Inc. Three-dimensional (3D) video presentation system providing interactive 3D presentation with personalized audio responses for multiple viewers
US6066075A (en) 1995-07-26 2000-05-23 Poulton; Craig K. Direct feedback controller for user interaction
US6765726B2 (en) 1995-11-06 2004-07-20 Impluse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
WO1997017598A1 (en) 1995-11-06 1997-05-15 Impulse Technology, Inc. System for continuous monitoring of physical activity during unrestricted movement
US7359121B2 (en) 1995-11-06 2008-04-15 Impulse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
US6430997B1 (en) 1995-11-06 2002-08-13 Trazer Technologies, Inc. System and method for tracking and assessing movement skills in multidimensional space
US6876496B2 (en) 1995-11-06 2005-04-05 Impulse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
US7038855B2 (en) 1995-11-06 2006-05-02 Impulse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
US6308565B1 (en) 1995-11-06 2001-10-30 Impulse Technology Ltd. System and method for tracking and assessing movement skills in multidimensional space
US6073489A (en) 1995-11-06 2000-06-13 French; Barry J. Testing and training system for assessing the ability of a player to complete a task
US6098458A (en) 1995-11-06 2000-08-08 Impulse Technology, Ltd. Testing and training system for assessing movement and agility skills without a confining field
US6283860B1 (en) 1995-11-07 2001-09-04 Philips Electronics North America Corp. Method, system, and program for gesture based option selection
US5933125A (en) 1995-11-27 1999-08-03 Cae Electronics, Ltd. Method and apparatus for reducing instability in the display of a virtual environment
US5641288A (en) 1996-01-11 1997-06-24 Zaenglein, Jr.; William G. Shooting simulating process and training device using a virtual reality display screen
US6152856A (en) 1996-05-08 2000-11-28 Real Vision Corporation Real time simulation using position sensing
US6173066B1 (en) 1996-05-21 2001-01-09 Cybernet Systems Corporation Pose determination and tracking by matching 3D objects to a 2D sensor
US5989157A (en) 1996-08-06 1999-11-23 Walton; Charles A. Exercising system with electronic inertial game playing
US6005548A (en) 1996-08-14 1999-12-21 Latypov; Nurakhmed Nurislamovich Method for tracking and displaying user's spatial position and orientation, a method for representing virtual reality for a user, and systems of embodiment of such methods
US5995649A (en) 1996-09-20 1999-11-30 Nec Corporation Dual-input image processor for recognizing, isolating, and displaying specific objects from the input images
US6111582A (en) 1996-12-20 2000-08-29 Jenkins; Barry L. System and method of image generation and encoding using primitive reprojection
US6128003A (en) 1996-12-20 2000-10-03 Hitachi, Ltd. Hand gesture recognition system and method
US6009210A (en) 1997-03-05 1999-12-28 Digital Equipment Corporation Hands-free interface to a virtual reality environment using head tracking
US6100896A (en) 1997-03-24 2000-08-08 Mitsubishi Electric Information Technology Center America, Inc. System for designing graphical multi-participant environments
US6124864A (en) 1997-04-07 2000-09-26 Synapix, Inc. Adaptive modeling and segmentation of visual image streams
US5877803A (en) 1997-04-07 1999-03-02 Tritech Mircoelectronics International, Ltd. 3-D image detector
US6215898B1 (en) 1997-04-15 2001-04-10 Interval Research Corporation Data processing system and method
US6327097B1 (en) 1997-04-17 2001-12-04 Zbig Vision Gesellschaft Fur Neue Bildgestaltung Mbh Optical imaging system and graphic user interface
US5949430A (en) 1997-05-20 1999-09-07 Microsoft Corporation Peripheral lenses for simulating peripheral vision on a display device
US6226396B1 (en) 1997-07-31 2001-05-01 Nec Corporation Object extraction method and system
US6188777B1 (en) 1997-08-01 2001-02-13 Interval Research Corporation Method and apparatus for personnel detection and tracking
US7042440B2 (en) 1997-08-22 2006-05-09 Pryor Timothy R Man machine interfaces and applications
US6289112B1 (en) 1997-08-22 2001-09-11 International Business Machines Corporation System and method for determining block direction in fingerprint images
US6496598B1 (en) 1997-09-02 2002-12-17 Dynamic Digital Depth Research Pty. Ltd. Image processing method and apparatus
US6215890B1 (en) 1997-09-26 2001-04-10 Matsushita Electric Industrial Co., Ltd. Hand gesture recognizing device
US6141463A (en) 1997-10-10 2000-10-31 Electric Planet Interactive Method and system for estimating jointed-figure configurations
US7746345B2 (en) 1997-10-15 2010-06-29 Hunter Kevin L System and method for generating an animatable character
US7184048B2 (en) 1997-10-15 2007-02-27 Electric Planet, Inc. System and method for generating an animatable character
US6130677A (en) 1997-10-15 2000-10-10 Electric Planet, Inc. Interactive computer vision system
USRE42256E1 (en) 1997-10-15 2011-03-29 Elet Systems L.L.C. Method and apparatus for performing a clean background subtraction
US6384819B1 (en) 1997-10-15 2002-05-07 Electric Planet, Inc. System and method for generating an animatable character
US6411744B1 (en) 1997-10-15 2002-06-25 Electric Planet, Inc. Method and apparatus for performing a clean background subtraction
US6072494A (en) 1997-10-15 2000-06-06 Electric Planet, Inc. Method and apparatus for real-time gesture recognition
US6101289A (en) 1997-10-15 2000-08-08 Electric Planet, Inc. Method and apparatus for unencumbered capture of an object
US6256033B1 (en) 1997-10-15 2001-07-03 Electric Planet Method and apparatus for real-time gesture recognition
US6181343B1 (en) 1997-12-23 2001-01-30 Philips Electronics North America Corp. System and method for permitting three-dimensional navigation through a virtual reality environment using camera-based gesture inputs
US6159100A (en) 1998-04-23 2000-12-12 Smith; Michael D. Virtual reality game
US6077201A (en) 1998-06-12 2000-06-20 Cheng; Chau-Yang Exercise bicycle
US7121946B2 (en) 1998-08-10 2006-10-17 Cybernet Systems Corporation Real-time head tracking system for computer games and other applications
US7460690B2 (en) 1998-08-10 2008-12-02 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US6681031B2 (en) 1998-08-10 2004-01-20 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US7668340B2 (en) 1998-08-10 2010-02-23 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US7036094B1 (en) 1998-08-10 2006-04-25 Cybernet Systems Corporation Behavior recognition system
US6950534B2 (en) 1998-08-10 2005-09-27 Cybernet Systems Corporation Gesture-controlled interfaces for self-service machines and other applications
US7684592B2 (en) 1998-08-10 2010-03-23 Cybernet Systems Corporation Realtime object tracking system
US6316934B1 (en) 1998-09-17 2001-11-13 Netmor Ltd. System for three dimensional positioning and tracking
US6256400B1 (en) 1998-09-28 2001-07-03 Matsushita Electric Industrial Co., Ltd. Method and device for segmenting hand gestures
US6661918B1 (en) 1998-12-04 2003-12-09 Interval Research Corporation Background estimation and segmentation based on range and color
US6147678A (en) 1998-12-09 2000-11-14 Lucent Technologies Inc. Video hand image-three-dimensional computer interface with multiple degrees of freedom
US7202898B1 (en) 1998-12-16 2007-04-10 3Dv Systems Ltd. Self gating photosurface
US6570555B1 (en) 1998-12-30 2003-05-27 Fuji Xerox Co., Ltd. Method and apparatus for embodied conversational characters with multimodal input/output in an interface device
US6373489B1 (en) 1999-01-12 2002-04-16 Schlumberger Technology Corporation Scalable visualization for interactive geometry modeling
US6363160B1 (en) 1999-01-22 2002-03-26 Intel Corporation Interface using pattern recognition and tracking
US7003134B1 (en) 1999-03-08 2006-02-21 Vulcan Patents Llc Three dimensional object pose estimation which employs dense depth information
US6299308B1 (en) 1999-04-02 2001-10-09 Cybernet Systems Corporation Low-cost non-imaging eye tracker system for computer control
US6503195B1 (en) 1999-05-24 2003-01-07 University Of North Carolina At Chapel Hill Methods and systems for real-time structured light depth extraction and endoscope using real-time structured light depth extraction
US6476834B1 (en) 1999-05-28 2002-11-05 International Business Machines Corporation Dynamic creation of selectable items on surfaces
US6873723B1 (en) 1999-06-30 2005-03-29 Intel Corporation Segmenting three-dimensional video images using stereo
US6738066B1 (en) 1999-07-30 2004-05-18 Electric Plant, Inc. System, method and article of manufacture for detecting collisions between video images generated by a camera and an object depicted on a display
US7760182B2 (en) 1999-08-01 2010-07-20 Subutai Ahmad Method for video enabled electronic commerce
US7113918B1 (en) 1999-08-01 2006-09-26 Electric Planet, Inc. Method for video enabled electronic commerce
US7050606B2 (en) 1999-08-10 2006-05-23 Cybernet Systems Corporation Tracking and gesture recognition system particularly suited to vehicular control applications
US6801637B2 (en) 1999-08-10 2004-10-05 Cybernet Systems Corporation Optical body tracker
US6798406B1 (en) 1999-09-15 2004-09-28 Sharp Kabushiki Kaisha Stereo images with comfortable perceived depth
US7367887B2 (en) 2000-02-18 2008-05-06 Namco Bandai Games Inc. Game apparatus, storage medium, and computer program that adjust level of game difficulty
US6633294B1 (en) 2000-03-09 2003-10-14 Seth Rosenthal Method and apparatus for using captured high density motion for animation
US7060957B2 (en) 2000-04-28 2006-06-13 Csem Centre Suisse D'electronique Et Microtechinique Sa Device and method for spatially resolved photodetection and demodulation of modulated electromagnetic waves
US6640202B1 (en) 2000-05-25 2003-10-28 International Business Machines Corporation Elastic sensor mesh system for 3-dimensional measurement, mapping and kinematics applications
US6731799B1 (en) 2000-06-01 2004-05-04 University Of Washington Object segmentation with background extraction and moving boundary techniques
US6788809B1 (en) 2000-06-30 2004-09-07 Intel Corporation System and method for gesture recognition in three dimensions using stereo imaging and color vision
US7898522B2 (en) 2000-07-24 2011-03-01 Gesturetek, Inc. Video-based image control system
US7227526B2 (en) 2000-07-24 2007-06-05 Gesturetek, Inc. Video-based image control system
US7058204B2 (en) 2000-10-03 2006-06-06 Gesturetek, Inc. Multiple camera control system
US7421093B2 (en) 2000-10-03 2008-09-02 Gesturetek, Inc. Multiple camera control system
US7555142B2 (en) 2000-10-03 2009-06-30 Gesturetek, Inc. Multiple camera control system
US7039676B1 (en) 2000-10-31 2006-05-02 International Business Machines Corporation Using video image analysis to automatically transmit gestures over a network in a chat or instant messaging session
US6539931B2 (en) 2001-04-16 2003-04-01 Koninklijke Philips Electronics N.V. Ball throwing assistant
US7259747B2 (en) 2001-06-05 2007-08-21 Reactrix Systems, Inc. Interactive video display system
US7834846B1 (en) 2001-06-05 2010-11-16 Matthew Bell Interactive video display system
US7452275B2 (en) 2001-06-29 2008-11-18 Konami Digital Entertainment Co., Ltd. Game device, game controlling method and program
US6937742B2 (en) 2001-09-28 2005-08-30 Bellsouth Intellectual Property Corporation Gesture activated home appliance
US7680298B2 (en) 2001-09-28 2010-03-16 At&T Intellectual Property I, L. P. Methods, systems, and products for gesture-activated appliances
US7607509B2 (en) 2002-04-19 2009-10-27 Iee International Electronics & Engineering S.A. Safety device for a vehicle
US8035612B2 (en) 2002-05-28 2011-10-11 Intellectual Ventures Holding 67 Llc Self-contained interactive video display system
US7348963B2 (en) 2002-05-28 2008-03-25 Reactrix Systems, Inc. Interactive video display system
US8035624B2 (en) 2002-05-28 2011-10-11 Intellectual Ventures Holding 67 Llc Computer vision based touch screen
US7170492B2 (en) 2002-05-28 2007-01-30 Reactrix Systems, Inc. Interactive video display system
US7710391B2 (en) 2002-05-28 2010-05-04 Matthew Bell Processing an image utilizing a spatially varying pattern
US8035614B2 (en) 2002-05-28 2011-10-11 Intellectual Ventures Holding 67 Llc Interactive video window
US7489812B2 (en) 2002-06-07 2009-02-10 Dynamic Digital Depth Research Pty Ltd. Conversion and encoding techniques
US20040109585A1 (en) * 2002-12-09 2004-06-10 Hai Tao Dynamic depth recovery from multiple synchronized video streams
US7576727B2 (en) 2002-12-13 2009-08-18 Matthew Bell Interactive directed light/sound system
US7436496B2 (en) 2003-02-03 2008-10-14 National University Corporation Shizuoka University Distance image sensor
US7593552B2 (en) 2003-03-31 2009-09-22 Honda Motor Co., Ltd. Gesture recognition apparatus, gesture recognition method, and gesture recognition program
US7590262B2 (en) * 2003-05-29 2009-09-15 Honda Motor Co., Ltd. Visual tracking using depth data
US8072470B2 (en) 2003-05-29 2011-12-06 Sony Computer Entertainment Inc. System and method for providing a real-time three-dimensional interactive environment
US7620202B2 (en) 2003-06-12 2009-11-17 Honda Motor Co., Ltd. Target orientation estimation using depth sensing
JP2005117475A (ja) 2003-10-09 2005-04-28 Canon Inc カメラサーバの色補正方法
US20050089213A1 (en) * 2003-10-23 2005-04-28 Geng Z. J. Method and apparatus for three-dimensional modeling via an image mosaic system
US7809167B2 (en) 2003-10-24 2010-10-05 Matthew Bell Method and system for processing captured image information in an interactive video display system
US7536032B2 (en) 2003-10-24 2009-05-19 Reactrix Systems, Inc. Method and system for processing captured image information in an interactive video display system
US20070247522A1 (en) 2003-12-18 2007-10-25 University Of Durham Method and Apparatus for Generating a Stereoscopic Image
US20090002483A1 (en) * 2004-03-02 2009-01-01 Kabushiki Kaisha Toshiba Apparatus for and method of generating image, and computer program product
US7379563B2 (en) 2004-04-15 2008-05-27 Gesturetek, Inc. Tracking bimanual movements
US7308112B2 (en) 2004-05-14 2007-12-11 Honda Motor Co., Ltd. Sign based human-machine interaction
US7704135B2 (en) 2004-08-23 2010-04-27 Harrison Jr Shelton E Integrated game system, method, and device
US20070109409A1 (en) * 2004-12-17 2007-05-17 Sehoon Yea Method and System for Processing Multiview Videos for View Synthesis using Skip and Direct Modes
US7702130B2 (en) 2004-12-20 2010-04-20 Electronics And Telecommunications Research Institute User interface apparatus using hand gesture recognition and method thereof
US7430312B2 (en) 2005-01-07 2008-09-30 Gesturetek, Inc. Creating 3D images of objects by illuminating with infrared patterns
US7570805B2 (en) 2005-01-07 2009-08-04 Gesturetek, Inc. Creating 3D images of objects by illuminating with infrared patterns
US7574020B2 (en) 2005-01-07 2009-08-11 Gesturetek, Inc. Detecting and tracking objects in images
US7379566B2 (en) 2005-01-07 2008-05-27 Gesturetek, Inc. Optical flow based tilt sensor
US8009871B2 (en) * 2005-02-08 2011-08-30 Microsoft Corporation Method and system to segment depth images and to detect shapes in three-dimensionally acquired data
US7598942B2 (en) 2005-02-08 2009-10-06 Oblong Industries, Inc. System and method for gesture based control system
US20080246759A1 (en) * 2005-02-23 2008-10-09 Craig Summers Automatic Scene Modeling for the 3D Camera and 3D Video
US7317836B2 (en) 2005-03-17 2008-01-08 Honda Motor Co., Ltd. Pose estimation based on critical point analysis
US20080246757A1 (en) * 2005-04-25 2008-10-09 Masahiro Ito 3D Image Generation and Display System
US7389591B2 (en) 2005-05-17 2008-06-24 Gesturetek, Inc. Orientation-sensitive signal output
US7560701B2 (en) 2005-08-12 2009-07-14 Mesa Imaging Ag Highly sensitive, fast pixel for use in an image sensor
US20080026838A1 (en) 2005-08-22 2008-01-31 Dunstan James E Multi-player non-role-playing virtual world games: method for two-way interaction between participants and multi-player virtual world games
US7551771B2 (en) * 2005-09-20 2009-06-23 Deltasphere, Inc. Methods, systems, and computer program products for acquiring three-dimensional range information
US20080252661A1 (en) 2005-09-27 2008-10-16 John Allen Hilton Interface for Computer Controllers
US7450736B2 (en) 2005-10-28 2008-11-11 Honda Motor Co., Ltd. Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers
US20070217682A1 (en) * 2006-03-15 2007-09-20 Matsushita Electric Industrial Co., Ltd. Method and apparatus for image conversion
US20070236510A1 (en) 2006-04-06 2007-10-11 Hiroyuki Kakuta Image processing apparatus, control method thereof, and program
US20070248350A1 (en) 2006-04-25 2007-10-25 Baker Henry H Automated process for generating a computed design of a composite camera comprising multiple digital imaging devices
US20070279415A1 (en) 2006-06-01 2007-12-06 Steve Sullivan 2D to 3D image conversion
WO2007148219A2 (en) * 2006-06-23 2007-12-27 Imax Corporation Methods and systems for converting 2d motion pictures for stereoscopic 3d exhibition
US7701439B2 (en) 2006-07-13 2010-04-20 Northrop Grumman Corporation Gesture recognition simulation system and method
WO2008020734A1 (en) * 2006-08-18 2008-02-21 Gwangju Institute Of Science And Technology A method and apparatus for encoding or decoding frames of different views in multiview video using global disparity
US7683954B2 (en) 2006-09-29 2010-03-23 Brainvision Inc. Solid-state image sensor
US7835569B2 (en) * 2006-10-13 2010-11-16 Apple Inc. System and method for raw image processing using conversion matrix interpolated from predetermined camera characterization matrices
EP2081376A1 (de) 2006-11-10 2009-07-22 Mitsubishi Electric Corporation Netzwerkbildsynthetisierendes anzeigesystem
US20080112610A1 (en) * 2006-11-14 2008-05-15 S2, Inc. System and method for 3d model generation
US20090315982A1 (en) 2006-11-22 2009-12-24 Alexander Schmidt Arrangement and method for the recording and display of images of a scene and/or an object
US7412077B2 (en) 2006-12-29 2008-08-12 Motorola, Inc. Apparatus and methods for head pose estimation and head gesture detection
US7729530B2 (en) 2007-03-03 2010-06-01 Sergey Antonov Method and apparatus for 3-D data input to a personal computer with a multimedia oriented operating system
US20090160934A1 (en) * 2007-07-23 2009-06-25 Disney Enterprises, Inc. Generation of three-dimensional movies with improved depth control
US7852262B2 (en) 2007-08-16 2010-12-14 Cybernet Systems Corporation Wireless mobile indoor/outdoor tracking system
WO2009069958A2 (en) 2007-11-29 2009-06-04 Gwangju Institute Of Science And Technology Method and apparatus for generating multi-viewpoint depth map, method for generating disparity of multi-viewpoint image
US20090158220A1 (en) * 2007-12-17 2009-06-18 Sony Computer Entertainment America Dynamic three-dimensional object mapping for user-defined control device
US20090169057A1 (en) * 2007-12-28 2009-07-02 Industrial Technology Research Institute Method for producing image with depth by using 2d images
US20090232389A1 (en) * 2008-03-12 2009-09-17 Samsung Electronics Co., Ltd. Image processing method and apparatus, image reproducing method and apparatus, and recording medium
US20090231425A1 (en) 2008-03-17 2009-09-17 Sony Computer Entertainment America Controller with an integrated camera and methods for interfacing with an interactive application
US20090284654A1 (en) * 2008-05-19 2009-11-19 Kabushiki Kaisha Toshiba Synchronizing signal control circuit and synchronizing signal control method
CN201254344Y (zh) 2008-08-20 2009-06-10 中国农业科学院草原研究所 植物标本及种子存贮器

Non-Patent Citations (35)

* Cited by examiner, † Cited by third party
Title
"International Search Report", Mailed Date: Sep. 27, 2011, Application No. PCT/US2011/020690, Filed Date: Jan. 10, 2011, pp. 8.
"Simulation and Training", 1994, Division Incorporated.
"The graPHIGS Programming Interface: Understanding Concepts", Retrieved at <<http://publib.boulder.ibm.com/infocenter/pseries/v5r3/topic/com.ibm.aix.graPHIGS/doc/phigsund/phigsund.pdf>>, Apr. 1994, pp. 338.
"Virtual High Anxiety", Tech Update, Aug. 1995, pp. 22.
Aggarwal et al., "Human Motion Analysis: A Review", IEEE Nonrigid and Articulated Motion Workshop, 1997, University of Texas at Austin, Austin, TX.
Azarbayejani et al., "Visually Controlled Graphics", Jun. 1993, vol. 15, No. 6, IEEE Transactions on Pattern Analysis and Machine Intelligence.
Breen et al., "Interactive Occlusion and Collusion of Real and Virtual Objects in Augmented Reality", Technical Report ECRC-95-02, 1995, European Computer-Industry Research Center GmbH, Munich, Germany.
Brogan et al., "Dynamically Simulated Characters in Virtual Environments", Sep./Oct. 1998, pp. 2-13, vol. 18, Issue 5, IEEE Computer Graphics and Applications.
European Patent Office, Communication pursuant to Article 94(3) EPC of European Patent Application No. 11740159. May 4, 2013, Germany, 5 pages.
European Patent Office, European Search Report of European Patent Application No. 11740159, Mar. 22, 2013, Germany, 5 pages.
Fisher et al., "Virtual Environment Display System", ACM Workshop on Interactive 3D Graphics, Oct. 1986, Chapel Hill, NC.
Freeman et al., "Television Control by Hand Gestures", Dec. 1994, Mitsubishi Electric Research Laboratories, TR94-24, Caimbridge, MA.
Granieri et al., "Simulating Humans in VR", The British Computer Society, Oct. 1994, Academic Press.
Hasegawa et al., "Human-Scale Haptic Interaction with a Reactive Virtual Human in a Real-Time Physics Simulator", Jul. 2006, vol. 4, No. 3, Article 6C, ACM Computers in Entertainment, New York, NY.
He, "Generation of Human Body Models", Apr. 2005, University of Auckland, New Zealand.
Hongo et al., "Focus of Attention for Face and Hand Gesture Recognition Using Multiple Cameras", Mar. 2000, pp. 156-161, 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France.
Isard et al., "Condensation-Conditional Density Propagation for Visual Tracking", 1998, pp. 5-28, International Journal of Computer Vision 29(1), Netherlands.
Kanade et al., "A Stereo Machine for Video-rate Dense Depth Mapping and Its New Applications", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996, pp. 196-202,The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
Kohler, "Special Topics of Gesture Recognition Applied in Intelligent Home Environments", In Proceedings of the Gesture Workshop, 1998, pp. 285-296, Germany.
Kohler, "Technical Details and Ergonomical Aspects of Gesture Recognition applied in Intelligent Home Environments", 1997, Germany.
Kohler, "Vision Based Remote Control in Intelligent Home Environments", University of Erlangen-Nuremberg/ Germany, 1996, pp. 147-154, Germany.
Livingston, "Vision-based Tracking with Dynamic Structured Light for Video See-through Augmented Reality", 1998, University of North Carolina at Chapel Hill, North Carolina, USA.
Miyagawa et al., "CCD-Based Range Finding Sensor", Oct. 1997, pp. 1648-1652, vol. 44 No. 10, IEEE Transactions on Electron Devices.
Pavlovic et al., "Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review", Jul. 1997, pp. 677-695, vol. 19, No. 7, IEEE Transactions on Pattern Analysis and Machine Intelligence.
Perona et al. "Scale-Space and Edge Detection Using Anisotropic Diffusion", IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.7, 1990, p. 629-639.
Qian et al., "A Gesture-Driven Multimodal Interactive Dance System", Jun. 2004, pp. 1579-1582, IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.
Rosenhahn et al., "Automatic Human Model Generation", 2005, pp. 41-48, University of Auckland (CITR), New Zealand.
Rotenberg, Steve, "Viewing & Perspective", Retrieved at <<http://graphics.ucsd.edu/courses/cse167—f05/CSE167—04.ppt>>, 2005, pp. 32.
Rotenberg, Steve, "Viewing & Perspective", Retrieved at >, 2005, pp. 32.
Shao et al., "An Open System Architecture for a Multimedia and Multimodal User Interface", Aug. 24, 1998, Japanese Society for Rehabilitation of Persons with Disabilities (JSRPD), Japan.
Sheridan et al., "Virtual Reality Check", Technology Review, Oct. 1993, pp. 22-28, vol. 96, No. 7.
Stevens, "Flights into Virtual Reality Treating Real World Disorders", The Washington Post, Mar. 27, 1995, Science Psychology, 2 pages.
Weickert et al., "Efficient and reliable schemes for nonlinear diffusion filtering", IEEE Trans. Image Proc., v.7 n.3, 1998, pp. 398-410.
Wren et al., "Pfinder: Real-Time Tracking of the Human Body", MIT Media Laboratory Perceptual Computing Section Technical Report No. 353, Jul. 1997, vol. 19, No. 7, pp. 780-785, IEEE Transactions on Pattern Analysis and Machine Intelligence, Caimbridge, MA.
Zhao, "Dressed Human Modeling, Detection, and Parts Localization", 2001, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130202161A1 (en) * 2012-02-05 2013-08-08 Primesense Ltd. Enhanced face detection using depth information
US8989455B2 (en) * 2012-02-05 2015-03-24 Apple Inc. Enhanced face detection using depth information
US20140282278A1 (en) * 2013-03-14 2014-09-18 Glen J. Anderson Depth-based user interface gesture control
US9389779B2 (en) * 2013-03-14 2016-07-12 Intel Corporation Depth-based user interface gesture control
US10386468B2 (en) 2015-03-05 2019-08-20 Hanwha Techwin Co., Ltd. Photographing apparatus and method

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